


import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__a 					=   logging.get_logger(__name__)







class 					__SCREAMING_SNAKE_CASE :


      def __init__(				self				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__		):
            lowercase      : Any        =		question_encoder
            lowercase      : List[Any]        =		generator
            lowercase      : str        =		self.question_encoder


      def 							__lowerCamelCase      (				self				,    SCREAMING_SNAKE_CASE__		):
            if os.path.isfile(_UpperCAmelCase		):
                  raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""		)
            os.makedirs(_UpperCAmelCase				,    exist_ok=_UpperCAmelCase		)
            lowercase      : Dict        =		os.path.join(_UpperCAmelCase				,    '''question_encoder_tokenizer'''		)
            lowercase      : Any        =		os.path.join(_UpperCAmelCase				,    '''generator_tokenizer'''		)
            self.question_encoder.save_pretrained(_UpperCAmelCase		)
            self.generator.save_pretrained(_UpperCAmelCase		)


      @classmethod
      def 							__lowerCamelCase      (				cls				,    SCREAMING_SNAKE_CASE__				,    **SCREAMING_SNAKE_CASE__		):
            # dynamically import AutoTokenizer
            from ..auto.tokenization_auto import AutoTokenizer

            lowercase      : Tuple        =		kwargs.pop('''config'''				,    _UpperCAmelCase		)

            if config is None:
                  lowercase      : Any        =		RagConfig.from_pretrained(_UpperCAmelCase		)

            lowercase      : List[Any]        =		AutoTokenizer.from_pretrained(
                _UpperCAmelCase				,    config=config.question_encoder				,    subfolder='''question_encoder_tokenizer'''		)
            lowercase      : int        =		AutoTokenizer.from_pretrained(
                _UpperCAmelCase				,    config=config.generator				,    subfolder='''generator_tokenizer'''		)
            return cls(question_encoder=_UpperCAmelCase				,    generator=_UpperCAmelCase		)


      def __call__(				self				,    *SCREAMING_SNAKE_CASE__				,    **SCREAMING_SNAKE_CASE__		):
            return self.current_tokenizer(*_UpperCAmelCase				,    **_UpperCAmelCase		)


      def 							__lowerCamelCase      (				self				,    *SCREAMING_SNAKE_CASE__				,    **SCREAMING_SNAKE_CASE__		):
            return self.generator.batch_decode(*_UpperCAmelCase				,    **_UpperCAmelCase		)


      def 							__lowerCamelCase      (				self				,    *SCREAMING_SNAKE_CASE__				,    **SCREAMING_SNAKE_CASE__		):
            return self.generator.decode(*_UpperCAmelCase				,    **_UpperCAmelCase		)


      def 							__lowerCamelCase      (				self		):
            lowercase      : List[Any]        =		self.question_encoder


      def 							__lowerCamelCase      (				self		):
            lowercase      : Optional[int]        =		self.generator






      def 							__lowerCamelCase      (				self				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__ = None				,    SCREAMING_SNAKE_CASE__ = None				,    SCREAMING_SNAKE_CASE__ = None				,    SCREAMING_SNAKE_CASE__ = "longest"				,    SCREAMING_SNAKE_CASE__ = None				,    SCREAMING_SNAKE_CASE__ = True				,    **SCREAMING_SNAKE_CASE__				,    ):
            warnings.warn(
                '''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
                '''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
                '''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
                '''details'''				,    _UpperCAmelCase				,    )
            if max_length is None:
                  lowercase      : Any        =		self.current_tokenizer.model_max_length
            lowercase      : str        =		self(
                _UpperCAmelCase				,    add_special_tokens=_UpperCAmelCase				,    return_tensors=_UpperCAmelCase				,    max_length=_UpperCAmelCase				,    padding=_UpperCAmelCase				,    truncation=_UpperCAmelCase				,    **_UpperCAmelCase				,    )
            if tgt_texts is None:
                  return model_inputs
            # Process tgt_texts
            if max_target_length is None:
                  lowercase      : Optional[int]        =		self.current_tokenizer.model_max_length
            lowercase      : Union[str, Any]        =		self(
                text_target=_UpperCAmelCase				,    add_special_tokens=_UpperCAmelCase				,    return_tensors=_UpperCAmelCase				,    padding=_UpperCAmelCase				,    max_length=_UpperCAmelCase				,    truncation=_UpperCAmelCase				,    **_UpperCAmelCase				,    )
            lowercase      : Any        =		labels['''input_ids''']
            return model_inputs






'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import _LazyModule


SCREAMING_SNAKE_CASE__     :						Dict	=  {'''tokenization_wav2vec2_phoneme''': ['''Wav2Vec2PhonemeCTCTokenizer''']}


if TYPE_CHECKING:
			from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
			import sys

			SCREAMING_SNAKE_CASE__     :						Dict	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




